Description: What do analytics on learning analytics tell us? How can we make sense of this emerging field’s historical roots, current state, and future trends, based on how its members report and debate their research? Challenge submissions should exploit the LAK Dataset for a meaningful purpose.

Description: In 2014, the focus of the second international DCLA workshop, like that of LAK14, will be on the intersection of discourse learning analytics research, theory and practice. Once researchers have developed and validated discourse-centric analytics, how can these be successfully deployed at scale to support learning?

Description: The focus of this workshop is on the potential benefits and
challenges of using specific computational methods to analyze
interactions in networked learning environments. The workshop
is designed for researchers and practitioners interested in
analytical studies of collaborative and networked learning.

Description: Learning analytics represents a field of growing interest amongst researchers. To date, many analytic techniques have been “imported” from related fields such as sociology, statistics, and web science. The appropriations from other fields has produced valuable insight into learning in networks, identifying at-risk learners, and improving analysis of discourse and concept development. A critical next stage for developing the sophistication of LA as a field is to engage with promising research in artificial intelligence, specifically machine learning, fields. This workshop will introduce students and faculty to machine learning and evaluate opportunities to apply supervised, unsupervised, and semi-supervised learning models to learning analytics. As learning analytics is concerned with a range of challenges, including network analysis, discourse analysis, prediction, adaptivity and personalization, #LAK14ML will explore specific ML solutions to various problems in the learning process and, more broadly, the system of education itself.

Workshop hashtag: #lak14ml

Tutorials

Note: Each tutorial links to their own information (or external site) in which they provide detailed information.

Description: Learning in the 21st century means thinking in complex and collaborative ways that are situated in a real world context. This tutorial will convene a community of researchers who are examining (or interested in examining) complex thinking using epistemic network analysis (ENA). Originally designed to assess epistemic frames—collections of skills, knowledge, identities, values, and ways of making decisions—in virtual game environments, ENA is now being used broadly to quantify the structure of connections that constitute complex thinking in large-scale datasets that record discourse (chat, email, and actions) in logfiles of many kinds. The tutorial will (1) introduce new users to this method, (2) provide further training and insight for those already using ENA, and (3) develop a broader community of users and, as a result, create opportunities for the advancement and improvement of ENA.

Description: The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists. We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research. The learning analytics community is heavily populated with researchers of these backgrounds, and we believe those that find themselves at the intersection of research, theory, and practice (the theme of LAK14) have a particular interest in expanding their knowledge of data-driven tools and techniques.